Comparing within-subject classification and regularization methods in fMRI for large and small sample sizes

• Published in: Human brain mapping Vol. 35; no. 9; pp. 4499 - 4517
• Main Authors: Churchill, Nathan W., Yourganov, Grigori, Strother, Stephen C.
• Format: Journal Article
• Language: English
• Published: New York, NY Blackwell Publishing Ltd 01.09.2014; Wiley-Liss; John Wiley & Sons, Inc; John Wiley and Sons Inc
• Subjects: Adult; Biological and medical sciences; BOLD fMRI; Brain - blood supply; Brain - physiology; Brain Mapping - methods; Cerebrovascular Circulation - physiology; data-driven metrics; Female; head motion; Humans; Investigative techniques, diagnostic techniques (general aspects); Logistic Models; Magnetic Resonance Imaging - methods; Male; Medical sciences; Miscellaneous; model optimization; multivariate analysis; Nervous system; Neuropharmacology; Oxygen - blood; Pharmacology. Drug treatments; physiological noise; preprocessing; Principal Component Analysis; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Reproducibility of Results; Sample Size; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; Support Vector Machine; Young Adult; Human; Motion; Nervous system diseases; Head; Radiodiagnosis; Sample size; Noise; preprocessing; head motion; physiological noise; Multivariate analysis; data-driven metrics; Nuclear magnetic resonance imaging; Optimization; BOLD fMRI; Regularization method; Classification; Models; model optimization; multivariate analysis

In recent years, a variety of multivariate classifier models have been applied to fMRI, with different modeling assumptions. When classifying high‐dimensional fMRI data, we must also regularize to improve model stability, and the interactions between classifier and regularization techniques are stil...